Intern, Machine Learning and Computational Biology

Intern, Machine Learning and Computational Biology - Hummingbird Bioscience
Full-time internship – [SG] based location
ABOUT HUMMINGBIRD BIOSCIENCE
Hummingbird Bioscience is a biotherapeutics company working at the interface of artificial intelligence and human innovation to discover and develop transformative medicines for hard-to-treat diseases. Hummingbird Bioscience’s computational and systems biology technologies have generated a pipeline of innovative clinical-stage monoclonal antibodies and antibody-drug conjugates in oncology and autoimmunity. At Hummingbird Bioscience, the commitment to rigorous science, teamwork, and intellectual integrity underpins our passion to accelerate the journey of new drugs from concept to clinic.
For more information, please visit www.hummingbirdbioscience.com, and follow Hummingbird Bioscience on LinkedIn, X (formerly Twitter), and YouTube.
ABOUT THE ROLE
The Bioinformatics team at Hummingbird Bioscience is expanding to meet the challenges in precision oncology. Our team delivers data-driven, actionable insights through the application of computational approaches to guide strategic decisions in clinical and drug development.
We are looking for a talented and highly motivated intern in Machine Learning and Computational Biology with a strong background in AI/ML model development and implementation, biological data analysis, analytical pipeline development, and an interest in contributing to the development of the next generation of cancer and immune-disease therapies.
KEY RESPONSIBILITIES
Research and finetune existing approaches to build LLM agents to automate research projects
Review, implement and benchmark state-of-the-art AI/ML approaches in drug discovery
Review and test Bayesian modeling approaches for clinical data analysis
Adapt/implement methods from publication to use with in-house datasets
Automate in silico routines for simplicity and interpretability to broader teams
Carry out high quality data analytics and reporting on in-house drug screenings as well as public datasets
Assist with C-level deck preparations
REQUIREMENTS AND QUALIFICATIONS
Currently enrolled in a Bachelor or Master’s Program in Computer Science, Statistics, Data analysis, Computational Biology or related science program
Experience in writing efficient and readable, well annotated scripts in Python for data analysis
Familiarity with molecular biology, biochemistry and computational biology methods
Ability to handle multiple projects concurrently is a big plus
OUR MISSION & VALUES
Our fast-growing biotechnology company is committed to discovering and developing important new drugs for cancer and autoimmune diseases, and living by our values: Excellence, Determination, Teamwork, Intellectual Integrity and Audacity.
JOIN US AT THE FRONTIER OF DRUG DISCOVERY AND DEVELOPMENT
We are looking for passionate and motivated individuals committed to solving important, complex problems. As an intern, you will have the opportunity to learn from a multi-disciplinary team and work towards transforming the therapeutic landscape for hard-to-treat diseases. We are committed to the personal and professional development of our team and offer robust learning and development programs. Hummingbird Bioscience understands the need for flexibility for our team and offers a generous paid time off program with flexibility to support employees through different life stages. We invest in our team’s health and offer a comprehensive and holistic employee assistance program. We foster a more collaborative, productive, and sociable culture with on-site lunches and snacks. Finally, we believe in creating social impact beyond our business through corporate social responsibility initiatives.
Are you ready to join us on our mission to discover and develop important new drugs for cancer and autoimmune disease? Click on ‘Apply for this Job’ to submit your application.
For further enquiries, please email us at careers@hummingbirdbio.com.

Employment type
Internship - Full Time

Work arrangement
No